HR001117S0011-Amendment-05.pdf

BAA# HR001117S0011-5 OFFENSIVE SWARM-ENABLED TACTICS (OFFSET)

Broad Agency Announcement OFFensive Swarm-Enabled Tactics (OFFSET) Amendment 5 – Swarm Sprint 4

Tactical Technology Office (TTO)

HR001117S0011

March 26, 2019

Defense Advanced Research Projects Agency Tactical Technology Office 675 North Randolph Street Arlington, VA 22203-2114

BAA# HR001117S0011-5 OFFENSIVE SWARM-ENABLED TACTICS (OFFSET) i

PART I: OVERVIEW INFORMATION

PART II: FULL TEXT OF ANNOUNCEMENT

I. Funding Opportunity Description

A. Program Vision B. Program Description C. OFFSET Swarm Sprints Structure D. Swarm Sprint Description

II. Award Information A. General Award Information B. Fundamental Research

III. Eligibility Information A. Eligible Applicants B. Organizational Conflicts of Interest C. Cost Sharing/Matching

IV. Application and Submission Information A. Address to Request Application Package B. Content and Form of Application Submission C. Funding Restrictions D. Other Submission Requirements

V. Application Review Information A. Evaluation Criteria B. Review of Proposals

VI. Award Administration Information A. Selection Notices and Notifications B. Administrative and National Policy Requirements C. Reporting D. Electronic Systems

VII. Agency Contacts VIII. Other Information

A. Frequently Asked Questions (FAQs) B. Associate Contractor Agreement (ACA) Clause C. List of Attachments

BAA# HR001117S0011-5 OFFENSIVE SWARM-ENABLED TACTICS (OFFSET) 1

PART I: OVERVIEW INFORMATION

• Federal Agency Name – Defense Advanced Research Projects Agency (DARPA), Tactical Technology Office (TTO)

• Funding Opportunity Title – OFFensive Swarm-Enabled Tactics (OFFSET)

• Announcement Type – Amendment

• Funding Opportunity Number – HR001117S0011

• Catalog of Federal Domestic Assistance Numbers (CFDA) – 12.910 Research and

Technology Development

• Dates o Posting Date: March 26 2019 o Proposal Due: May 1 2019, 1300 (Eastern)

**Calls for Swarm Sprinter proposals will be issued periodically (6-month intervals are anticipated) as amendments to this BAA at future dates yet to be determined.

These calls may be issued at any time up to one year from the posting date.

• Concise description of the funding opportunity – The goal of OFFSET Swarm Sprints is to create focused breakthroughs in swarm technologies to be integrated into the OFFSET Swarm Systems Architecture. The specific topics of interest for this amendment are (1) the creation and implementation of potential future synthetic technologies in OFFSET virtual environments to enable exploration of novel swarm tactics; and (2) the application of artificial intelligence methods to accelerate and aid the design of advanced swarm tactics that are robust in realistic operational settings.

• Anticipated individual awards – DARPA intends to award up to fifteen Swarm Sprinter awards in this Swarm Sprint call. No individual award is anticipated to be above $450K (including all options proposed).

• Types of instruments that may be awarded – Procurement contract, cooperative agreement, or Other Transaction.

• Agency contact o Points of Contact

The BAA Coordinator for this effort can be reached at:

OFFSET@darpa.mil mailto:OFFSET@darpa.mil

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PART II: FULL TEXT OF ANNOUNCEMENT

I. Funding Opportunity Description

This publication constitutes a Broad Agency Announcement (BAA) as contemplated in Federal Acquisition Regulation (FAR) 6.102(d)(2) and 35.016 and 2 CFR § 200.203. Any resultant award negotiations will follow all pertinent law and regulation, and any negotiations and/or awards for procurement contracts will use procedures under FAR 15.4, Contract Pricing, as specified in the

BAA.

The Defense Advanced Research Projects Agency (DARPA) is soliciting innovative proposals to expand the operational effectiveness of combat forces utilizing unmanned swarm systems.

Proposed research should investigate innovative approaches that enable revolutionary advances in science, devices, or systems. Specifically excluded is research that primarily results in evolutionary improvements to the existing state of practice.

The following information is for those wishing to respond to this BAA.

A. Program Vision

This section is unchanged by this amendment; please refer to initial posting of BAA#

HR001117S0011.

B. Program Description

HR001117S0011.

B.1. Core Elements of the OFFSET Swarm System This section is unchanged by this amendment; please refer to initial posting of BAA#

HR001117S0011.

B.2. Defining the Swarm Systems Architecture This section is unchanged by this amendment; please refer to initial posting of BAA#

HR001117S0011.

B.3. OFFSET Swarm Ecosystem This section is unchanged by this amendment; please refer to initial posting of BAA#

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B.3.a. Swarm Systems Integrators The initial release of this BAA sought well-balanced and expert teams, referred to as Swarm Systems Integrators (SSIs), capable of holistically designing, developing, and deploying the envisioned OFFSET swarm system architecture, as detailed above, to include:

• an extensible game-based architecture to enable design and integration of swarm tactics,

• a community-fostering forum (e.g., swarm tactics exchange) for curation of swarm tactics and an automated workflow for their assessment, BAA# HR001117S0011-5 OFFENSIVE SWARM-ENABLED TACTICS (OFFSET) 3

• immersive interactive modalities for intuitive and robust teams of humans and swarm systems,

• aggressive experimentation and systems integration efforts for realizing swarm capabilities.

B.3.b. Swarm Sprinters This BAA amendment seeks proposals for OFFSET Swarm Sprinters. Swarm Sprinters may comprise third-party developers and/or users. These Swarm Sprinters must be able to design swarm tactics and/or requisite supporting elements as well as develop and integrate them according to the specifications set forth by the architectures (as developed by the Swarm Systems Integrators).

The Government would like to engage with a wider developer and user audience for this capability;

therefore, OFFSET anticipates soliciting proposals for Swarm Sprinters over multiple intervals to conduct “sprints” or rapid technology development efforts to generate novel swarm tactics and to accelerate additional enabling technologies while ensuring operational relevance of the capability-based experiments.

Candidate sprint topics may include more narrowly defined thrusts (such as, for example, the design of swarm tactics relevant to searching a building for hidden caches) or broad emphasis areas (such as integration of novel maneuver specifications for heterogeneous swarm systems). Possible sprints may also emphasize desired improvements to the overall architecture, including enabling toolkits and/or interface enhancements; payload development studies; swarm deployment technologies; and/or other focused investigations based on continued monitoring of advancing technologies relevant to the OFFSET program interests.

Proposals for Swarm Sprinters are expected to be solicited through multiple BAA amendments at periodic (approximately six-month) intervals. The development sprints comprise focused short-term efforts, with key objectives to deliver frequent product increments and integrate them with the open-source architectures provided by the Swarm Systems Integrators. Upon award, Swarm Sprinters are expected to work with the Swarm Systems Integrator(s) to further enhance the swarm systems architecture and its demonstrable capabilities. The Swarm Systems Integrator(s) are also expected to work collaboratively and openly with the Swarm Sprinters to enable smooth integration and encourage user feedback. Communication and sharing of material between Swarm Systems Integrators and Swarm Sprinters are planned to occur through a Government-Sponsored Enterprise System, Defense Intelligence Information Enterprise (DI2E). Access to this site requires at least one member of the team to be a U.S. citizen. To promote this open and transparent teamwork OFFSET will use Associate Contractor Agreements (see Section VIII.B). In addition, Swarm Systems Integrator(s), their subcontractor, and any subsidiaries are prohibited from proposing to or being awarded as a Swarm Sprinter.

B.3.c. OFFSET Government Team This section is unchanged by this amendment; please refer to initial posting of BAA#

HR001117S0011.

B.4. OFFSET Metrics

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C. OFFSET Swarm Sprints Structure

The overall OFFSET program is structured as a 42-month effort. The first phase extends for 18 months, with the subsequent two phases conducted over 12 months each. The program’s agile development cycle is embodied in a Swarm Systems Integrator-Sprinter ecosystem that focuses development and integration efforts toward six biannual capability-based experiments. Regular field exercises provide opportunities to test and evaluate integration of technologies and provide more direct engagement between warfighters and technologists.

The OFFSET Swarm Sprints are intended to provide technology injects and enhancements to the swarm systems architectures and their demonstrable capabilities. Such injects provide means to:

• respond to the evolving needs of the program,

• leverage emerging technologies as they become available, and

• pursue opportunities for technology transitions.

To accomplish this objective, the Government anticipates issuing multiple calls for Swarm Sprints over the 42-month duration of the OFFSET program. The duration of each Swarm Sprint is generally expected to be six months, with an additional three-month option period to facilitate integration activities. The initiation and execution of these Swarm Sprints are anticipated to align with the respective capability-based experiments, as dictated by the Swarm Sprint Topics. This will ensure close interaction with the Swarm Systems Integrators and facilitate the demonstration of the proposed component and integrated technologies.

These Swarm Sprints are organized into five thrust areas or high-level research areas of interest to OFFSET, to include Swarm Tactics; Swarm Autonomy; Human-Swarm Teaming; Virtual Environment; and Physical Testbed. Table 1 provides a description of each of these thrust areas.

Table 1: Descriptions of Swarm Sprint Thrust Areas

Thrust Area Description

Swarm Tactics Design, development, assessment, and integration of swarm tactics aligned with OFFSET mission objectives and vignettes

Swarm Autonomy

Development of systems enablers for enhanced swarm autonomy, including both hardware and software along with associated tactics, primitives, and algorithms

Human-Swarm Teaming

Development, enhancement, and integration of novel human interface technologies, interaction modalities, and swarm grammar frameworks

Virtual Environment

Design, integration, and assessment of synthetic entities within the virtual game-based environment that enhance the platforms, sensors, networking, adversary, environmental, and other component elements

Physical Testbed

Design and integration of physically implementable technologies for augmenting physical testbed entities, to include additional platforms, sensors, infrastructure, computation, and experimental design

The Swarm Sprints are categorized further into Core Sprints and Ad Hoc Sprints, the former relating to those Swarm Sprints anticipated to occur at regular six-month intervals in each of the

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thrust areas, and the latter allowing for instantiating “on-demand” swarm interest topics aligned with a specified thrust area.

Figure 1: Notional overall Swarm Sprint schedule. BAA Amendments announcing Swarm Sprints are anticipated to be released at approximate six-month intervals. Core Sprint timelines are subject to change.

Swarm Sprints in the five previously mentioned thrust areas will provide critical enhancements to the overall OFFSET program. Swarm Sprint performers in both Core and Ad Hoc Sprint activities are likely to be motivated by a number of incentivizing elements of the OFFSET program structure. Given that the outcomes of each Swarm Sprint will offer novel features and/or new capabilities for one or more of the Swarm Systems Integrators’ respective architectures, these integrators are inclined to adopt and showcase the most compelling and innovative Swarm Sprinter-developed contributions as part of their integration efforts. Further, the Swarm Sprinters may directly help inform and shape the evolution of the swarm technologies of interest to OFFSET, both through their close interactions and feedback with the Swarm Systems Integrator teams and also via their active contribution of feature requests, bug reports, and updates to the OFFSET code and knowledge base.

D. Swarm Sprint Description

This BAA amendment solicits proposals for a Core Sprint in Virtual Environments (Section D.1), and also for an Ad Hoc Sprint in the topic area of Applications of A.I. (Section D.2). Proposers may respond to more than one Swarm Sprint topic area; however, a separate, standalone proposal is required for each Swarm Sprint topic area.

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D.1. Core Sprint | Swarm Systems Advances through Synthetic Technologies

Sprint At-A-Glance

Name: Swarm Systems Advances through Synthetic Technologies

Thrust Area: Virtual Environment

Purpose: Develop and implement synthetic capabilities in simulation representing potential future technologies to enable and demonstrate novel swarm tactics

Figure 2: Synthetic technologies – “what if” capabilities implemented in OFFSET virtual environments, e.g., distributed “see-through-wall” sensors – may inspire and enable entirely new classes of swarm tactics, potentially informing both future technology investments and novel concepts of operations.

D.1.a. Swarm Sprint Description

i. Virtual Environments Enhancement Description

OFFSET aims to accelerate swarm system development through the tight integration of virtual environments with physical swarm platforms and architectures to foster the creation and discovery of innovative swarm tactics. This Swarm Sprint focuses on enhancements to the virtual environments currently under active development to gain insights into potential future swarm technologies. Instantiated as virtual simulation models, these technologies may disrupt existing – or offer entirely new – avenues for “what if” exploration of novel swarm capabilities, especially in the context of the significant complexity afforded by autonomous swarms.

Key research questions to be addressed by innovative responses to this Swarm Sprint BAA are:

1. What future technologies might have the potential, if eventually realizable for integration into physical swarm systems, to disrupt existing approaches to conducting urban operations?

2. How would such future technologies be leveraged in the form of swarm tactics to dramatically improve the swarm’s effectiveness operating in complex urban environments?

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The scope of this Swarm Sprint is to envision and identify disruptive technologies that may not (yet) be realizable in physical swarm systems but have the potential, in the form of innovative swarm tactics, to enable and inform future breakthrough capabilities for autonomous swarms.

The Swarm Sprinter is asked to create and implement virtual models of future swarm-enabled technologies to be integrated into one or both of the Swarm Systems Integrators’ simulation environments (based on Unreal and Unity3D game engines), and to demonstrate the (simulated) efficacy and performance of these synthetic technologies through Swarm Sprinter-developed or Sprinter-modified swarm tactics and/or associated swarm primitives and swarm algorithms.

ii. Operational Context Description

To envision an OFFSET-empowered future operational backdrop, consider the scenario where the swarm systems form the “tactical swarm vanguard,” which serves the role of an advance party ahead of the main operating force (e.g., an infantry company or platoon). In this context of “recce” or “armed recon,” the swarm vanguard conducts surveillance of the area of operations, provides tactical situational awareness, prepares the battlespace, and/or initiates relevant activities to support the main unit’s mission objectives. The main force elements may advance to augment the swarm’s present activities, offering occasions for combined actions involving both human and autonomous swarm forces, as well as enabling the swarm to continue pressing forward as appropriate or necessitated by the mission.

Referencing Marine Corps Warfighting Publication (MCWP) 12-10B.1, an offensive urban operation can be divided into a series of four phases: reconnoiter the objective; isolate the objective; secure a foothold; and seize the objective, which can be described as:

• Reconnoiter the Objective: Gathering of intelligence such as avenues of approach, supply routes, ingress and egress points, infrastructure and utilities, locations of enemy combatants, and other information that may not be apparent in existing databases or maps;

• Isolate the Objective: Denying the enemy the ability to maneuver or reinforce by impeding the flow of manpower, supplies, weapons, and communications;

• Secure a Foothold: Providing the attacking force with a position from which they can continue the assault through the objective area;

• Seize the Objective: Pushing forces to clear or control the objective area, including consolidation of the objective area and reorganization for future tasking.

For the purposes of this Swarm Sprint, DARPA is interested in concepts and technologies relevant to swarm operations conducted in any one or more of these mission phases. As such, Swarm Sprint proposals should clearly reference the specific mission contexts and operational challenges that the proposed synthetic technologies address, with discussion of appropriate metrics for assessing the envisioned operational impact.

D.1.b. Technical Description

OFFSET’s simulators offer virtual testbeds for designing and developing swarm tactics in the context of small-unit urban operations, i.e., in built-up terrain and urban settings. In addition to realistic scenes (e.g., roads, buildings), models of swarm platforms within the Unreal- or Unity3D-based simulators are designed to emulate (and interface with) the actual physical platforms as

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closely as possible. These integrated Swarm Systems Architectures offer a more seamless transfer of promising swarm tactics between simulator and physical systems with minimal integration costs, thereby reducing time and risk to field test these new (albeit necessarily physically-realizable) swarm technologies.

The OFFSET virtual environments in and of themselves, however, offer unbounded opportunities to simulate synthetic-only swarm capabilities to inform swarm tactics design relevant for autonomous swarms of the future. In other words, unconstrained by the current physical limitations (such as specific sensors, platform endurance, payload capacity, communications types, etc.) of the existing OFFSET air or ground robotic platforms, the virtual environments enable exploration of novel synthetic technologies to reveal new potential development and investment areas of research.

Swarm Sprinters are expected to offer contributions that are suitable and/or compatible for implementation in one or both of the OFFSET Swarm Systems Architectures. Proposed approaches should leverage Swarm Systems Integrator-developed software architectures and game-based synthetic environments that offer one or more virtual worlds in which relevant urban operational scenarios can be explored. Potential Swarm Sprinters are encouraged to reach out to the Swarm Systems Integrator teams to learn about their respective ongoing developments of virtual environments, models, and tools, etc. to distinguish the proposer’s potential contributions and/or accentuate their own innovations within the OFFSET architecture(s).

Strong proposals should clearly define and characterize the virtual models proposed, to include description of component elements (e.g., 3D graphical objects, physics-based sensor models), the anticipated fidelity of the models (including discussion of any theoretical or experimental underpinnings), and whether any supporting validation studies or results exist for the proposed synthetic technology. In addition, the novelty of the developed swarm tactics should be made clear in the context of the operational need and/or technology gap addressed.

Synthetic Technologies for Autonomous Swarms

DARPA is soliciting the development of synthetic technologies representing potential future capabilities for autonomous heterogeneous swarms operating in complex urban environments.

Proposed technologies could be near-term advances that are being prototyped in laboratories or far-term ideas that are primarily conceptual but physically grounded.

In particular, OFFSET seeks synthetic swarm technologies which uniquely leverage attributes of swarm systems (e.g., distributed sensing, dispersed computing, heterogeneity, composable effects) that offer breakthroughs in future swarm-enabled capabilities such as, but not limited to:

- stand-off RF imaging sensor arrays to scan, e.g., interior floorplans of high-rise buildings using distributed swarm sources and/or receivers;

- novel passive (e.g., non-RF) communications technologies for swarm networking and information sharing in multi-path and indoor/outdoor settings;

- enhanced sensor payloads and edge computing architectures for collaborative multi-spectral perception in degraded or cluttered visual environments;

- coordinated light or signal emitters to create illusory effects robust to multiple view angles;

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- modular combination of multiple platforms for enhanced interactions with or negotiation of the environment, e.g., multiple aerial mobile manipulators for distributed lift, linked ground platforms for debris/stairwell navigation;

- distributed kinetic effects in support of collective actions such as coordinated breaching or smoke screening for force protection.

Highly innovative proposals should clearly articulate the technical basis for the proposed synthetic technology, to include theoretical, empirical, and/or prototype results indicating the potential feasibility and level of readiness of the technology, as well as an estimate of the time horizon for the technology to reach sufficient maturity for potential integration into actual swarm systems.

Such discussion may be quantitatively informed by trends in miniaturization, fabrication methods, computing availability, connectivity, commercialization, etc., and/or innovations and investments in academic and laboratory research.

Proposals only offering trivial simulation enhancements, such as longer battery life, faster platforms, or perfect communications, or synthetic technologies presented without sufficient technical merit or lacking scientific or engineering justifications, are deemed out of scope.

Assessing Future-Enabled Swarm Tactics

The proposed synthetic swarm technologies provide powerful new opportunities to discover novel swarm tactics, which are anticipated to lead to dramatically improved swarm mission performance.

Such technological disruptions have the potential to change, e.g., how swarm systems are employed in the battlefield, the size and/or composition of (human) ground forces required, or even how entire urban missions are conducted. Swarm Sprinters should investigate and develop swarm tactics (and/or the underlying swarm autonomy elements, i.e., swarm algorithms and swarm primitives) which take advantage of the proposed synthetic technologies to lead to such innovative breakthroughs.

In conjunction with the development of such future-enabled swarm tactics, Swarm Sprinters should also develop a simulation analysis framework (and any associated modifications to the simulation environment) to conduct quantitative and/or comparative assessments of the proposed swarm tactics. Such simulation studies should be used to highlight the anticipated dramatic improvement in swarm system performance over existing approaches. Swarm Sprinters are highly encouraged to define and use relevant measures of performance (which emphasize the assessment of the synthetic technology itself) and measures of effectiveness (which emphasize the assessment of the technology’s effectiveness in the context of a mission) in conducting these simulation studies.

Potential simulation capabilities to be developed may include game-engine plugins or software utilities to enable, for example: input of user-generated statistical experimental designs; headless and/or faster-than-real-time execution of Monte Carlo simulation runs; and/or collection of massive simulation-based swarm datasets.

High-quality contributions to one or both OFFSET virtual environments should also enable extensible simulation analyses of the proposed synthetic technologies in other use cases beyond the Swarm Sprinter’s self-developed swarm tactics. In other words, the simulation analysis framework should ideally allow for assessment of third-party swarm tactics and/or swarm

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autonomy elements which make use of the proposed synthetic technologies.

Synthetic Swarm Technology Integration

The proposed Swarm Sprinter effort should substantively leverage and build upon one or both of the existing OFFSET virtual environments, which are implemented in Unreal and Unity3D game engines. More specifically, any proposed game objects, relevant assets, simulation scripts or code, etc. should be directly compatible with the Swarm Systems Integrator-developed simulations, and ideally within the context of the existing simulation architectures, i.e., using their software development toolchain and/or build procedures.

Additionally, noting that the underlying OFFSET Swarm Systems Architectures are designed to enable transparent interplay between virtual and physical robotic sensors and systems, e.g., through the use of Robot Operating System and/or other interfaces, synthetic technology development efforts (i.e., virtual models, data processing, and/or message passing) which account for such potential interfaces are considered by DARPA to be highly desirable implementations.

Swarm Sprinters should seek to frequently demonstrate minimally viable products throughout their development efforts, representing continued progress in integrating their proposed synthetic technologies and associated swarm tactics into the OFFSET virtual environments. The proposed integration approach should describe the expected frequency and types of active engagement (e.g., technical discussions, bug reports, pull requests) with the Swarm Systems Integrators’ developers to facilitate such demonstrations through, e.g., software delivery, simulation videos, etc.

D.1.c. Programmatic Description

This Swarm Sprint is envisioned as a six-month base period-of-performance plus three-month option effort. Included in this six-month base effort is the active use and integration with one or both of the Swarm Systems Integrator architectures and virtual environments; Swarm Sprinters should initiate early engagement to gain access, familiarize, and test in these OFFSET simulators to maximize productive development time during this base period. The proposed level of effort and travel costs should clearly state and reflect the proposer’s technical and program management approach to integrate their proposed contributions with one or both of the Swarm Systems Integrators’ architectures.

The focus of the option period is to, if exercised, enable further opportunities to illustrate the impact and/or viability of the synthetic technologies, through one of the proposed following activities:

1. Integration and demonstration of the developed swarm tactics in an upcoming OFFSET capability-based experimentation event, anticipated to occur in Spring 2020. In this case, the proposer should outline a concept and test plan for including, e.g., surrogate sensors, fiducial markers, artificial or scripted interactions, or combined use of the virtual environment, to enable representative demonstration of the synthetic technology and/or its associated swarm tactics in live field experiments working with one or both of the Swarm Systems Integrators.

The purpose of this choice of option task would be to take advantage of field test settings to showcase the (synthetic) technology’s potential for improving mission performance and/or informing new concept of operations, offering a more tangible vision for potential transition to future end-users.

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2. Development of a preliminary systems designs and rigorous trade analyses that detail an implementable technical approach towards physically realizing the proposed synthetic technologies. In this case, the proposer should describe relevant component and systems design and engineering specifications, identify current technology gaps, articulate an aggressive research plan to potentially overcome those gaps, and discuss achievable performance metrics (as compared with performance parameters assumed by the synthetic technology used during the base period). The purpose of this choice of option task would be to construct a potential development and/or investment roadmap that would accelerate the transformation of the proposed synthetic technologies into realizable ones, thereby enabling the eventual implementation of the use cases, i.e., swarm tactics, envisioned and demonstrated during the base period.

D.1.d. Deliverables for this Swarm Sprint

It is anticipated that deliverables for this Swarm Sprint will consist of the following at a minimum:

Deliverable Frequency Format Description

Synthetic Technology Data Package

Every two

(2) months after contract award

Contractor format

Provide latest versions of software (i.e., source code) and supporting dependencies as appropriate, which should include clear and usable documentation, be well commented, and follow programming best practices such that others experienced in the field can readily use and interpret the functionality of the code.

Swarm Tactics Code

Every three

(3) months after contract award

Contractor format

The code and supporting library submissions should include clear documentation, be well commented, and follow programming best practices such that others experienced in the field can readily use and interpret the functionality of the code. Submissions should also include swarm tactics analysis scripts.

Swarm Tactics Videos As required

MP4,

MPG, AVI,

MOV

Simulation-based video(s) for developed swarm tactic should be submitted. The video(s) should clearly demonstrate the functionality of each swarm tactic, including any user inputs and all unique variants of a given swarm tactic.

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Integration Plan

One (1) month after contract award

Contractor format

Detail how the Swarm Sprint efforts will develop within the context of one or both Swarm Systems Integrators’ architectures and virtual environment interfaces. This document should reflect firm technical understanding, tasks, and schedule of integration activities, including engagement with Swarm Systems Integrators.

Progress Report Monthly Microsoft PowerPoint

Include a summary of results and technical accomplishments, planned actions for the next reporting period, descriptive and graphical representations of work being completed, and all relevant financial information, including updates to financial spend plan (i.e., by task and by month), current and cumulative expenditures, open commitments, and explanation of deltas between planned and actual costs/plans.

Technical Data Package

15 days prior to contract end

Contractor format

Provide any supporting and generated technical data, to include: simulation elements, e.g., 3D graphics and models;

generated and/or captured videos;

benchmark sensor datasets; and simulation validation analysis and results.

Final Report

15 days prior to contract end

Microsoft Word

Provide a detailed description of the developed synthetic technologies and swarm tactics. The report should also provide a detailed quantitative analysis of relevant metrics, e.g., swarm performance of each swarm tactic, ease of integration, and extensibility of the developed technologies.

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D.2. Ad Hoc Sprint | A.I. for Accelerating Swarm Tactics Design

D.2.a. Swarm Sprint Description

i. A.I. for Advancing Autonomous Swarm Systems

Existing and emerging applications of artificial intelligence (A.I.) potentially offer transformative possibilities in the discovery, optimization, and learning of novel capabilities for highly complex systems, such as autonomous swarms, operating in similarly complex environments, such as urban settings. As relevant to OFFSET, A.I. methods can be broadly applied to explore and elicit innovative swarm tactics, and can further leverage the OFFSET Swarm Systems Architectures, with their tightly integrated virtual environments and physical swarm testbeds. In doing so, such A.I. research efforts have a unique opportunity to accelerate advances in swarm system technologies and to do so at the large scales and settings of operational relevance.

A.I. has been used in many areas of robotics, multi-agent systems, and swarm intelligence, including fields of planning, resource allocation, control, and sensing, to name a few. In this Swarm Sprint, DARPA seeks innovative efforts which build upon and extend beyond such previous research efforts to address the following research questions:

1. How can A.I. accelerate, inform, and/or aid the discovery and design of advanced swarm tactics relevant to complex real-world operations?

2. Under what conditions can A.I.-enabled swarm systems operate robustly, i.e., in the presence of high uncertainty, dynamic environments, adversarial behaviors, etc.?

Sprint At-A-Glance

Name: A.I. for Accelerating Swarm Tactics Design

Thrust Area: Applications of Artificial Intelligence

Purpose: Discover and learn novel swarm tactics through application of A.I. and machine learning using enhancements of OFFSET virtual environments

Figure 3: Artificial intelligence can accelerate and aid the discovery of novel swarm tactics, leveraging enhanced integrated virtual environments for operationally guided swarm tactics development.

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The scope of this Swarm Sprint encompasses two primary development task areas, which address

(1) the application of an A.I. framework that interfaces with the OFFSET simulation architectures to learn, discover, and assess swarm tactics in game-based scenarios; and (2) the implementation of relevant challenging contexts in the game-based simulation environments, including dynamic and adversarial settings, with which to test and strengthen these A.I.-generated swarm tactics.

ii. Operational Context Description

Consider the urban mission of conducting an urban raid (relevant to OFFSET Vignette 2; see previous postings of BAA#HR001117S0011), in which friendly units must maneuver and approach a specified location (e.g., a high-rise building); push forces to enter, search, and clear the interior of the objective in the presence of highly uncertain, likely mobile, adversarial conditions;

and simultaneously observe and defend the immediate surrounding area from enemy units. For OFFSET, this mission is scoped to occur within an area of four square city blocks (0.5 mile by 0.5 mile) over a mission duration of 1-2 hours employing a heterogeneous (air and ground) swarm of 100 agents.

This illustrative mission offers numerous relevant analogues to the game-based A.I. research communities, including “capture the flag”-type games requiring search for and capture of an opponent’s flag or similar variations. For this Swarm Sprint topic area, the context of an operationally realistic, potentially adaptive if not adversarial environment, offers fertile ground for exploration of swarm tactics via the application of diverse A.I. methods.

D.2.b. Technical Description

The application of artificial intelligence to robotics and multi-agent systems has been in continued development since the genesis of these fields. However, due to the complexity and cost of developing and constructing (let alone fielding and operating) large-scale, physical swarm system testbeds, much of the prior swarm-related A.I. research focused on software-only, simulation-based development, in many cases often far outpacing systems development of physical autonomous swarms.

OFFSET seeks to narrow this reality gap between virtual and physical swarms through its Swarm Systems Architectures, which offer a potential connection between A.I.-based generation of disruptive swarm tactics and realizable capabilities manifested in physical swarm systems.

The objectives of this Swarm Sprint are thus two-fold. First, DARPA seeks application of A.I. and machine learning methods to generate (and/or aid a human to better design) swarm tactics, specifically in the context of OFFSET’s complex urban settings with large, heterogeneous teams of air and ground robots. The second desired outcome is the creation and implementation of simulation frameworks for enabling accelerated and efficient “stress testing” of those A.I.-generated swarm tactics in tractable yet operationally relevant, complex and dynamic environments, including adversarial settings, through enhancements in the OFFSET virtual environment(s).

Strong proposals should not only demonstrate a solid grasp of the interplay between swarm systems design attributes (e.g., swarm size, heterogeneity, agent capabilities, collective complexity) in applying one or more A.I. approaches to discovering new swarm tactics, but also should highlight an integrated approach for iteratively and/or interactively “escalating” the robustness of the generated swarm tactics in the face of increasing complexity, uncertainty, and/or

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adversity in the virtual world(s).

Artificial Intelligence for Swarm Tactics

The complexity of swarm systems offers (dauntingly) many design variables for constructing high-level swarm tactics, especially when also including the intricacies of operational and tactical mission plans and their execution. The urban raid mission of interest to this Swarm Sprint comprises numerous phases or actions to be undertaken by the autonomous swarm, carried out in the form of a composition of swarm tactics. This Swarm Sprint topic area envisions the application of A.I. to generate new swarm tactics which can either be individually substituted as component phases of the swarm mission, or to create whole sequences (a.k.a. courses of action) of swarm tactics through different A.I. methods to conduct the entire urban mission from start to finish.

The Swarm Sprinter may choose from the myriad A.I. approaches, based on their respective strengths and/or prerequisites, to include (but not limited to):

- Neuroevolutionary approaches;

- Reinforcement learning (RL), including deep RL or inverse RL;

- Transfer learning, e.g., from other video games based on A.I. and/or human play data;

- Interactive A.I. approaches, where users can influence the A.I. to achieve more desirable or tailored solutions.

In addition to articulating the potential swarm tactics to be addressed in the proposed effort, proposals should discuss in detail the relevant strengths and weaknesses of the chosen A.I.

approach (e.g., computational requirements, availability and/or size of datasets, robustness to uncertainty, time to generate a model), especially in the context of how they may impact potential integration and implementation in physical swarm systems.

Swarm Sprinters should leverage existing models for the swarm platforms and available simulation models for sensors (e.g., RGB cameras, LIDARs), platform specifications (e.g., speed, endurance), communications (e.g., message sizes, transmit rates), etc. as implemented in the OFFSET virtual environments, recognizing that such models may be changed or refined as the program progresses.

Proposed swarm tactics are anticipated (but not required) to relate to one or more of the following swarm capabilities: collective movement and/or action coordination; distributed perception;

models of communications and networking; and/or multi-agent scheduling or optimization of resources.

Given potential limitations in availability and/or access to all swarm autonomy elements (i.e., swarm primitives and swarm algorithms), proposers may offer to use surrogate models for, e.g., collision avoidance, simplified auction algorithms, etc., within their proposed implementations.

Ratcheting Up Robustness of A.I.-generated Swarm Tactics

Just as simulation can be used to discover new swarm tactics, it can also be used to expand the operating envelope of these swarm tactics for increased relevance to realistic and challenging environments such as those of interest to OFFSET. Critical to successful use of any A.I.-generated

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swarm tactic is an understanding of how various operational conditions either elevate or exacerbate its behavior and performance within the context of the mission.

This development task asks Swarm Sprinters to offer simulator enhancements to one or both of the OFFSET virtual environments that best enable exploration and demonstration of the robustness of the proposed A.I.-generated swarm tactics. By jointly considering the A.I. development approach with these enhancements for creating or manipulating the virtual test environments to “harden” the proposed swarm tactics, the Swarm Sprinter is expected to (1) more quickly identify the swarm tactics’ operational limits; (2) more effectively target any weaknesses or fragility through the virtual environment; and (3) guide the A.I. process to yield more robust swarm tactics.

Examples of potential enhancements to the virtual environment(s) may include, but are not limited to, implementation of:

- Automated scenario/world builder for urban environments with, e.g., parameterized urban density, city layout, or building size distributions;

- Dynamic non-player characters (NPCs), e.g., pedestrians, vehicles, traffic patterns;

- Dynamic hazards or environmental effects, e.g., moving obstacles, weather, smoke/fire;

- Adversarial NPCs, e.g., adaptive and/or reactive “red team” entities (e.g., “red swarm”) that attack, maneuver, or evade to diminish or confuse the OFFSET (“blue”) swarm;

- Adversarial environmental effects, e.g., RF jamming (comms or GPS), sensor dazzling.

Strong proposals will describe a robust set of virtual environment enhancements to be developed and implemented during the period of performance, as well as an integrated test framework for iterative development of A.I.-generated swarm tactics. The proposal should articulate Swarm Sprinter-defined performance metrics with which to conduct technically rigorous assessments and analyses of the developed swarm tactics, including a detailed “escalation” test plan for increasing the severity of the virtual test environments as the iteratively developed swarm tactics meet each test exit criteria.

Other relevant simulation improvements may include software architecture upgrades, such as migration to cloud or parallel computing resources, or implementation of automated test servers, insofar as they support the proposer’s response to the Swarm Sprint objectives outlined above.

Proposals which offer only software architectural improvements are not of interest to DARPA.

Iterative Integration of A.I.-enabled Swarm Tactics

The proposed Swarm Sprinter effort should substantively leverage and build upon one or both of the existing OFFSET virtual environments, which are implemented in Unreal and Unity3D game engines. More specifically, any proposed game objects, relevant assets, simulation scripts or code, and virtual environment enhancements, etc. should be directly compatible with the Swarm Systems Integrator-developed simulations, and ideally within the context of the existing simulation architectures, i.e., using their software development toolchain and/or build procedures.

Swarm Sprinters should seek to frequently demonstrate minimally viable products throughout their development efforts, representing continued progress in developing A.I.-generated swarm tactics and implementing simulator enhancements in the OFFSET virtual environments. The proposed

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integration approach should describe the expected frequency and types of active engagement (e.g., technical discussions, bug reports, pull requests) with the Swarm Systems Integrators’ developers to facilitate such demonstrations through, e.g., software delivery, simulation videos, etc.

D.2.c. Programmatic Description

This Swarm Sprint is envisioned as a six-month base period-of-performance plus three-month option effort. Included in this six-month base effort is the active use and integration with one or both of the Swarm Systems Integrator architectures and virtual environments; Swarm Sprinters should initiate early engagement to gain access, familiarize, and test in these OFFSET simulators to maximize productive development time during this base period. The proposed level of effort and travel costs should clearly state and reflect the proposer’s technical and program management approach to integrate their proposed contributions with one or both of the Swarm Systems Integrators’ architectures.

The focus of the option period is to, if exercised, enable integration and demonstration of the A.I.-developed swarm tactics in an upcoming OFFSET capability-based experimentation event, anticipated to occur in Spring 2020. The proposer should outline a concept and test plan to enable robust demonstration of the A.I.-developed swarm tactics in live field experiments working with one or both of the Swarm Systems Integrators.

D.2.d. Deliverables for this Swarm Sprint

It is anticipated that deliverables for this Swarm Sprint will consist of the following at a minimum:

Deliverable Frequency Format Description

A.I. Technical Package

Every two

(2) months after contract award

Contractor format

Provide latest versions of the A.I. model, and associated software (i.e., source code), relevant data, parameters, and supporting dependencies (e.g., scripts). This technical package should be entirely sufficient to reproduce/recreate the model, and should include clear and usable documentation and reference to version numbers for any/all open-source software packages used (e.g., A.I. libraries).

Swarm Tactics Code

Every three

(3) months after contract award

Contractor format

The code and supporting library submissions should include clear documentation, be well commented, and follow programming best practices such that others experienced in the field can readily use and interpret the functionality of the code. Submissions should also include swarm tactics analysis scripts.

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Swarm Tactics Videos As required MP4, MPG, AVI, MOV

Simulation- or physical systems-based video(s) for each swarm tactic should be submitted. The video(s) should clearly demonstrate the functionality of each swarm tactic, including any user inputs and all unique variants of a given swarm tactic.

Integration Plan (Virtual

Environment)

One (1) month after contract award

Contractor format

Describe intended tasks and schedule for development in one or both of the Swarm Systems Integrators’ virtual environments.

This document must include a timeline for multiple releases (e.g., pull requests) of virtual environment enhancements, a refined swarm tactics “escalation” test plan, and schedule for periodic integration activities, including engagement (e.g., technical discussions) with Swarm Systems Integrators.

Integration Plan (Field Experiment)

Four (4) months after contract award

Contractor format

Detail the integration requirements for implementing and testing the A.I.-generated swarm tactics in one or both of the Swarm Systems Integrators’ physical swarm systems, architectures, and platforms. This document must describe all tasks and schedule items relevant to field test/demonstration during the Swarm Sprint Option period of performance (if exercised).

Timeline should include interface tests, hardware integration, and coordination with the OFFSET (USG) Experimentation Team for logistics and field test planning.

Progress Report Monthly Microsoft PowerPoint

Include a summary of results and technical accomplishments, planned actions for the next reporting period, descriptive and graphical representations of work being completed, and all relevant financial information, including updates to financial spend plan (i.e., by task and by month), current and cumulative expenditures, open commitments, and explanation of deltas between planned and actual costs/plans.

Technical Data Package

15 days prior to contract end

Contractor format

Provide any supporting and generated technical data, to include: simulation elements, e.g., 3D graphics and models;

generated and/or captured videos;

benchmark sensor datasets; and simulation validation analysis and results.

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Final Report 15 days prior to contract end

Microsoft Word

Provide a detailed description of the A.I.

methodology used, the generated swarm tactics, and the virtual environment enhancements. The report should also provide a detailed quantitative analysis of relevant metrics, e.g., swarm performance of each swarm tactic, ease of integration, and extensibility of the developed…